library(Seurat)
library(ape)
library(RColorBrewer)
library(ggplot2)
#Data file
data.integrated <- readRDS("../Inputs/IntegratedData.rds")
#Color panels
maccols <- brewer.pal(n=8, name="Blues")[c(-1,-3,-5,-7)]
monocols <- c("#ff8ade","#e324ad")
dccols <- brewer.pal(n=9, name="Greens")[-1]
tcols <- brewer.pal(n=8, name="Reds")[-1]
nkcols <- c("#876149","#6e3f22")
bcols <- brewer.pal(n=4, name="Purples")[-1]
othcols <- c("#71a157","#00c5cc","#a18e25","#b282b3")
strcols <- brewer.pal(n=4, name="Oranges")[-1]
wccols = c("#878787", "#518db6","#94cc73","#e96b53")
cols <- c(maccols,monocols,dccols,tcols,nkcols,bcols,othcols,strcols)
llcols <- c("#4292C6","#ff8ade","#238B45","#EF3B2C","#876149","#9E9AC8","#71a157","#00c5cc","#a18e25","#b282b3","#FD8D3C")
Figure 2 introduces our single cell sequencing approach and the simple classification of cell types using cell-type exclusive markers and targeted surface protein analysis.
Schematic of CITE-Seq approach
DimPlot(data.integrated, group.by="lowlevel2", cols=llcols) + NoLegend() + labs(title="")
DimPlot(data.integrated, group.by="highlevel2", cols=cols) + NoLegend() + labs(title="")
#For labels
DimPlot(data.integrated, group.by="highlevel2", cols=cols) + labs(title="")
Idents(data.integrated) <- data.integrated$lowlevel2
DefaultAssay(data.integrated) <- "RNA"
VlnPlot(data.integrated,
features=c("Fcgr1","Ear2","Clec9a","Cd3d","Klrb1c","Ms4a1","Jchain","Mcpt4","S100a8","Gata3","Col3a1"),
stack=T,
flip=T,
fill.by="ident",
cols = llcols) +
labs(x="") +
NoLegend()
DefaultAssay(data.integrated) <- "ADT"
Idents(data.integrated) <- data.integrated$lowlevel2
VlnPlot(data.integrated,
features=c("MAC2","CD64","CD11b","CD11c","CD80","CD3","CD4","CD19","CD39","NK1.1"),
stack=T,
flip=T,
fill.by="ident",
cols = llcols) +
labs(x="") +
NoLegend()
DefaultAssay(data.integrated) <- "integrated"
Idents(data.integrated) <- data.integrated$highlevel2
data.integrated <- BuildClusterTree(object=data.integrated,
assay="integrated")
data.tree <- Tool(object = data.integrated, slot = "BuildClusterTree")
ape::plot.phylo(x = data.tree, direction = "downwards", label.offset=60, cex=0.8)
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.3.5 RColorBrewer_1.1-2 ape_5.5 SeuratObject_4.0.2
## [5] Seurat_4.0.3
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_2.0-2 deldir_0.2-10
## [4] ellipsis_0.3.2 ggridges_0.5.3 spatstat.data_2.1-0
## [7] farver_2.1.0 leiden_0.3.8 listenv_0.8.0
## [10] ggrepel_0.9.1 fansi_0.5.0 codetools_0.2-18
## [13] splines_4.1.0 knitr_1.33 polyclip_1.10-0
## [16] jsonlite_1.7.2 ica_1.0-2 cluster_2.1.2
## [19] png_0.1-7 uwot_0.1.10 shiny_1.6.0
## [22] sctransform_0.3.2 spatstat.sparse_2.0-0 compiler_4.1.0
## [25] httr_1.4.2 assertthat_0.2.1 Matrix_1.3-4
## [28] fastmap_1.1.0 lazyeval_0.2.2 later_1.2.0
## [31] htmltools_0.5.1.1 tools_4.1.0 igraph_1.2.6
## [34] gtable_0.3.0 glue_1.4.2 RANN_2.6.1
## [37] reshape2_1.4.4 dplyr_1.0.7 Rcpp_1.0.7
## [40] scattermore_0.7 jquerylib_0.1.4 vctrs_0.3.8
## [43] nlme_3.1-152 lmtest_0.9-38 xfun_0.24
## [46] stringr_1.4.0 globals_0.14.0 mime_0.11
## [49] miniUI_0.1.1.1 lifecycle_1.0.0 irlba_2.3.3
## [52] goftest_1.2-2 future_1.21.0 MASS_7.3-54
## [55] zoo_1.8-9 scales_1.1.1 spatstat.core_2.3-0
## [58] promises_1.2.0.1 spatstat.utils_2.2-0 parallel_4.1.0
## [61] yaml_2.2.1 reticulate_1.20 pbapply_1.4-3
## [64] gridExtra_2.3 sass_0.4.0 rpart_4.1-15
## [67] stringi_1.7.3 highr_0.9 rlang_0.4.11
## [70] pkgconfig_2.0.3 matrixStats_0.59.0 evaluate_0.14
## [73] lattice_0.20-44 ROCR_1.0-11 purrr_0.3.4
## [76] tensor_1.5 patchwork_1.1.1 htmlwidgets_1.5.3
## [79] labeling_0.4.2 cowplot_1.1.1 tidyselect_1.1.1
## [82] parallelly_1.27.0 RcppAnnoy_0.0.18 plyr_1.8.6
## [85] magrittr_2.0.1 R6_2.5.0 generics_0.1.0
## [88] DBI_1.1.1 pillar_1.6.1 withr_2.4.2
## [91] mgcv_1.8-36 fitdistrplus_1.1-5 survival_3.2-11
## [94] abind_1.4-5 tibble_3.1.2 future.apply_1.7.0
## [97] crayon_1.4.1 KernSmooth_2.23-20 utf8_1.2.1
## [100] spatstat.geom_2.2-2 plotly_4.9.4.1 rmarkdown_2.9
## [103] grid_4.1.0 data.table_1.14.0 digest_0.6.27
## [106] xtable_1.8-4 tidyr_1.1.3 httpuv_1.6.1
## [109] munsell_0.5.0 viridisLite_0.4.0 bslib_0.2.5.1